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Xu W, Fu YL, Zhu D. ResNet and its application to medical image processing: Research progress and challenges. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107660. [PMID: 37320940 DOI: 10.1016/j.cmpb.2023.107660] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/04/2023] [Accepted: 06/07/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Deep learning, a novel approach and subset of machine learning, has drawn a growing amount of attention from computer vision researchers in recent years. This method has drawn a lot of interest because of its extraordinary ability to interpret medical pictures, especially when combined with residual neural networks, which have helped to progress the field. METHODS In this paper, the following research is carried out on the residual network. First, the research status of ResNet in the medical field is introduced. The fundamental idea behind the residual neural network is then explained, along with the residual unit, its many structures, and the network architecture. Second, four aspects of the widespread use of residual neural networks in medical image processing are discussed: lung tumor, diagnosis of skin diseases, diagnosis of breast diseases, and diagnosis of diseases of the brain. Finally, the main issues and ResNet's future development in the area of processing medical images are discussed. RESULTS In the area of medical graph processing, residual neural networks have made strides and have had success in the clinical auxiliary diagnosis of serious illnesses such as lung tumors, breast cancer, skin conditions, and cardiovascular and cerebrovascular diseases. CONCLUSION We thoroughly sorted out the most recent developments in residual neural network research and their use in medical image processing, which serves as a crucial point of reference for this field of study. It offers a helpful reference for further promoting the application and research of the ResNet model in the field of medical image processing by summarising the application status and issues of the ResNet model in the field of medical image processing and putting forwards some future development directions.
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Affiliation(s)
- Wanni Xu
- Xiamen Academy of Arts and Design, Fuzhou University, Xiamen 361021, China
| | - You-Lei Fu
- Department of Computer Information Engineering, Nanchang Institute of Technology, Nanchang 330044, China.
| | - Dongmei Zhu
- College of Information Management, Nanjing Agricultural University, Nanjing 210095, China
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Qiu D, Cheng Y, Wang X. Medical image super-resolution reconstruction algorithms based on deep learning: A survey. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 238:107590. [PMID: 37201252 DOI: 10.1016/j.cmpb.2023.107590] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 03/21/2023] [Accepted: 05/05/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND AND OBJECTIVE With the high-resolution (HR) requirements of medical images in clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution (LR) medical images have become a research hotspot. This type of method can significantly improve image SR without improving hardware equipment, so it is of great significance to review it. METHODS Aiming at the unique SR reconstruction algorithms in the field of medical images, based on subdivided medical fields such as magnetic resonance (MR) images, computed tomography (CT) images, and ultrasound images. Firstly, we deeply analyzed the research progress of SR reconstruction algorithms, and summarized and compared the different types of algorithms. Secondly, we introduced the evaluation indicators corresponding to the SR reconstruction algorithms. Finally, we prospected the development trend of SR reconstruction technology in the medical field. RESULTS The medical image SR reconstruction technology based on deep learning can provide more abundant lesion information, relieve the expert's diagnosis pressure, and improve the diagnosis efficiency and accuracy. CONCLUSION The medical image SR reconstruction technology based on deep learning helps to improve the quality of medicine, provides help for the diagnosis of experts, and lays a solid foundation for the subsequent analysis and identification tasks of the computer, which is of great significance for improving the diagnosis efficiency of experts and realizing intelligent medical care.
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Affiliation(s)
- Defu Qiu
- Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yuhu Cheng
- Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xuesong Wang
- Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
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Gong P, Cheng L, Zhang Z, Meng A, Li E, Chen J, Zhang L. Multi-omics integration method based on attention deep learning network for biomedical data classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107377. [PMID: 36739624 DOI: 10.1016/j.cmpb.2023.107377] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 01/06/2023] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Integrating multi-omics data for the comprehensive analysis of the biological processes in human diseases has become one of the most challenging tasks of bioinformatics. Deep learning (DL) algorithms have recently become one of the most promising multi-omics data integration analysis methods. However, existing DL-based studies almost integrate the multi-omics data by concatenation in the input data space or the learned feature space, ignoring the correlations between patients and omics. METHODS We propose a novel multi-omics integration method, called Multi-omics Attention Deep Learning Network (MOADLN), which is used for biomedical data classification. Firstly, for each type of omics data, we use three fully-connected layers and the self-attention mechanism to reduce dimensionality, and construct the correlations between patients, respectively. Then, we apply the feature vector learned from self-attention to generate the initial category labels. Secondly, for the initial label predicted of each omics data, we use an effective Multi-Omics Correlation Discovery Network (MOCDN) to learn the cross-omic correlations in the label space. Finally, we use the softmax classifier for label prediction. RESULTS We demonstrate that our method outperforms several state-of-the-art methods on two datasets with mRNA expression data, DNA methylation data, and miRNA expression data. In addition, we identified essential biomarkers of relevant diseases by MOADLN, and the generality of MOADLN is also demonstrated in the KIRP and KIRC datasets. CONCLUSIONS MOADLN jointly explores correlations between patients in intra-omics and correlations of cross-omics in label space, which is an effective DL-based classification of biomedical data.
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Affiliation(s)
- Ping Gong
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, CN, China.
| | - Lei Cheng
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, CN, China
| | - Zhiyuan Zhang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, CN, China
| | - Ao Meng
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, CN, China
| | - Enshuo Li
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, CN, China
| | - Jie Chen
- Department of Radiation Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, CN, China
| | - Longzhen Zhang
- Department of Radiation Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, CN, China
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Zhu D, He H, Wang D. Feedback attention network for cardiac magnetic resonance imaging super-resolution. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107313. [PMID: 36739626 DOI: 10.1016/j.cmpb.2022.107313] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 12/05/2022] [Accepted: 12/14/2022] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Atrial fibrillation (AF) is a common clinical arrhythmia with a high disability and mortality rate. Improving the resolution of atrial structure and its changes in patients with AF is very important for understanding and treating AF. METHODS Aiming at the problems of previous deep learning-based image super-resolution (SR) reconstruction methods simply deepening the network, loss of upsampling information, and difficulty in the reconstruction of high-frequency information, we propose the Feedback Attention Network (FBAN) for cardiac magnetic resonance imaging (CMRI) super-resolution. The network comprises a preprocessing module, a multi-scale residual group module, an upsampling module, and a reconstruction module. The preprocessing module uses a convolutional layer to extract shallow features and dilate the number of channels of the feature map. The multi-scale residual group module adds a multi-channel network, a mixed attention mechanism, and a long and short skip connection to expand the receptive field of the feature map, improve the multiplexing of multi-scale features and strengthen the reconstruction of high-frequency information. The upsampling module adopts the sub-pixel method to upsample the feature map to the target image size. The reconstruction module consists of a convolutional layer, which is used to restore the number of channels of the feature map to the original number to obtain the reconstructed high-resolution (HR) image. RESULTS Furthermore, the test results on the public dataset of CMRI show that the HR images reconstructed by the FBAN method not only have a good improvement in reconstructed edge and texture information but also have a good improvement in the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) objective evaluation indicators. CONCLUSION Compared with the local magnified image, the edge information of the FBAN method reconstructed image has been enhanced, more high-frequency information of the CMRI is restored, the texture details are less lost, and the reconstructed image is less blurry. Overall, the reconstructed image has a lighter feeling of smearing, and the visual experience is more apparent and sharper.
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Affiliation(s)
- Dongmei Zhu
- College of Information Management, Nanjing Agricultural University, Nanjing 210095, China
| | - Hongxu He
- College of Information Management, Nanjing Agricultural University, Nanjing 210095, China
| | - Dongbo Wang
- College of Information Management, Nanjing Agricultural University, Nanjing 210095, China.
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Song Z, Qiu D, Zhao X, Lin D, Hui Y. Channel attention generative adversarial network for super-resolution of glioma magnetic resonance image. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107255. [PMID: 36462426 DOI: 10.1016/j.cmpb.2022.107255] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 11/03/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Glioma is the most common primary craniocerebral tumor caused by the cancelation of glial cells in the brain and spinal cord, with a high incidence and cure rate. Magnetic resonance imaging (MRI) is a common technique for detecting and analyzing brain tumors. Due to improper hardware and operation, the obtained brain MRI images are low-resolution, making it difficult to detect and grade gliomas accurately. However, super-resolution reconstruction technology can improve the clarity of MRI images and help experts accurately detect and grade glioma. METHODS We propose a glioma magnetic resonance image super-resolution reconstruction method based on channel attention generative adversarial network (CGAN). First, we replace the base block of SRGAN with a residual dense block based on the channel attention mechanism. Second, we adopt a relative average discriminator to replace the discriminator in standard GAN. Finally, we add the mean squared error loss to the training, consisting of the mean squared error loss, the L1 norm loss, and the generator's adversarial loss to form the generator loss function. RESULTS On the Set5, Set14, Urban100, and glioma datasets, compared with the state-of-the-art algorithms, our proposed CGAN method has improved peak signal-to-noise ratio and structural similarity, and the reconstructed glioma images are more precise than other algorithms. CONCLUSION The experimental results show that our CGAN method has apparent improvements in objective evaluation indicators and subjective visual effects, indicating its effectiveness and superiority.
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Affiliation(s)
- Zhaoyang Song
- College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China; Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China; National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China
| | - Defu Qiu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xiaoqiang Zhao
- College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China; Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China; National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
| | - Dongmei Lin
- College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China; Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China; National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China
| | - Yongyong Hui
- College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China; Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China; National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China
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Xu Y, Dai S, Song H, Du L, Chen Y. Multi-modal brain MRI images enhancement based on framelet and local weights super-resolution. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:4258-4273. [PMID: 36899626 DOI: 10.3934/mbe.2023199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Magnetic resonance (MR) image enhancement technology can reconstruct high-resolution image from a low-resolution image, which is of great significance for clinical application and scientific research. T1 weighting and T2 weighting are the two common magnetic resonance imaging modes, each of which has its own advantages, but the imaging time of T2 is much longer than that of T1. Related studies have shown that they have very similar anatomical structures in brain images, which can be utilized to enhance the resolution of low-resolution T2 images by using the edge information of high-resolution T1 images that can be rapidly imaged, so as to shorten the imaging time needed for T2 images. In order to overcome the inflexibility of traditional methods using fixed weights for interpolation and the inaccuracy of using gradient threshold to determine edge regions, we propose a new model based on previous studies on multi-contrast MR image enhancement. Our model uses framelet decomposition to finely separate the edge structure of the T2 brain image, and uses the local regression weights calculated from T1 image to construct a global interpolation matrix, so that our model can not only guide the edge reconstruction more accurately where the weights are shared, but also carry out collaborative global optimization for the remaining pixels and their interpolated weights. Experimental results on a set of simulated MR data and two sets of real MR images show that the enhanced images obtained by the proposed method are superior to the compared methods in terms of visual sharpness or qualitative indicators.
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Affiliation(s)
- Yingying Xu
- School of Electronics and Information Engineering, Taizhou University, Taizhou 318000, China
| | - Songsong Dai
- School of Electronics and Information Engineering, Taizhou University, Taizhou 318000, China
| | - Haifeng Song
- School of Electronics and Information Engineering, Taizhou University, Taizhou 318000, China
| | - Lei Du
- School of Electronics and Information Engineering, Taizhou University, Taizhou 318000, China
| | - Ying Chen
- School of Electronics and Information Engineering, Taizhou University, Taizhou 318000, China
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Zhu D, Sun D, Wang D. Dual attention mechanism network for lung cancer images super-resolution. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107101. [PMID: 36367483 DOI: 10.1016/j.cmpb.2022.107101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/29/2022] [Accepted: 08/29/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Currently, the morbidity and mortality of lung cancer rank first among malignant tumors worldwide. Improving the resolution of thin-slice CT of the lung is particularly important for the early diagnosis of lung cancer screening. METHODS Aiming at the problems of network training difficulty and low utilization of feature information caused by the deepening of network layers in super-resolution (SR) reconstruction technology, we propose the dual attention mechanism network for single image super-resolution (SISR). Firstly, the feature of a low-resolution image is extracted directly to retain the feature information. Secondly, several independent dual attention mechanism modules are constructed to extract high-frequency details. The introduction of residual connections can effectively solve the gradient disappearance caused by network deepening, and long and short skip connections can effectively enhance the data features. Furthermore, a hybrid loss function speeds up the network's convergence and improves image SR restoration ability. Finally, through the upsampling operation, the reconstructed high-resolution image is obtained. RESULTS The results on the Set5 dataset for 4 × enlargement show that compared with traditional SR methods such as Bicubic, VDSR, and DRRN, the average PSNR/SSIM is increased by 3.33 dB / 0.079, 0.41 dB / 0.007 and 0.22 dB / 0.006 respectively. The experimental data fully show that DAMN can better restore the image contour features, obtain higher PSNR, SSIM, and better visual effect. CONCLUSION Through the DAMN reconstruction method, the image quality can be improved without increasing radiation exposure and scanning time. Radiologists can enhance their confidence in diagnosing early lung cancer, provide a basis for clinical experts to choose treatment plans, formulate follow-up strategies, and benefit patients in the early stage.
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Affiliation(s)
- Dongmei Zhu
- College of Information Management, Nanjing Agricultural University, Nanjing, 210095, China; School of Information Engineering, Shandong Huayu University of Technology, Dezhou, 253034, China
| | - Degang Sun
- School of Information Engineering, Shandong Huayu University of Technology, Dezhou, 253034, China
| | - Dongbo Wang
- College of Information Management, Nanjing Agricultural University, Nanjing, 210095, China.
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Qiu D, Cheng Y, Wang X. Improved generative adversarial network for retinal image super-resolution. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:106995. [PMID: 35970055 DOI: 10.1016/j.cmpb.2022.106995] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 04/30/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE The retina is the only organ in the body that can use visible light for non-invasive observation. By analyzing retinal images, we can achieve early screening, diagnosis and prevention of many ophthalmological and systemic diseases, helping patients avoid the risk of blindness. Due to the powerful feature extraction capabilities, many deep learning super-resolution reconstruction networks have been applied to retinal image analysis and achieved excellent results. METHODS Given the lack of high-frequency information and poor visual perception in the current reconstruction results of super-resolution reconstruction networks under large-scale factors, we present an improved generative adversarial network (IGAN) algorithm for retinal image super-resolution reconstruction. Firstly, we construct a novel residual attention block, improving the reconstruction results lacking high-frequency information and texture details under large-scale factors. Secondly, we remove the Batch Normalization layer that affects the quality of image generation in the residual network. Finally, we use the more robust Charbonnier loss function instead of the mean square error loss function and the TV regular term to smooth the training results. RESULTS Experimental results show that our proposed method significantly improves objective evaluation indicators such as peak signal-to-noise ratio and structural similarity. The obtained image has rich texture details and a better visual experience than the state-of-the-art image super-resolution methods. CONCLUSION Our proposed method can better learn the mapping relationship between low-resolution and high-resolution retinal images. This method can be effectively and stably applied to the analysis of retinal images, providing an effective basis for early clinical treatment.
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Affiliation(s)
- Defu Qiu
- Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
| | - Yuhu Cheng
- Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
| | - Xuesong Wang
- Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
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Qiu D, Cheng Y, Wang X. End-to-end residual attention mechanism for cataractous retinal image dehazing. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106779. [PMID: 35397410 DOI: 10.1016/j.cmpb.2022.106779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 03/20/2022] [Accepted: 03/24/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Cataract is one of the most common causes of vision loss. Light scattering due to clouding of the lens in cataract patients makes it extremely difficult to image the retina of cataract patients with fundus cameras, resulting in a serious decrease in the quality of the retinal images taken. Furthermore, the age of cataract patients is generally too old, in addition to cataracts, the patients often have other retinal diseases, which brings great challenges to experts in the clinical diagnosis of cataract patients using retinal imaging. METHODS In this paper, we present the End-to-End Residual Attention Mechanism (ERAN) for Cataractous Retinal Image Dehazing, which it includes four modules: encoding module, multi-scale feature extraction module, feature fusion module, and decoding module. The encoding module encodes the input cataract haze image into an image, facilitating subsequent feature extraction and reducing memory usage. The multi-scale feature extraction module includes a hole convolution module, a residual block, and an adaptive skip connection, which can expand the receptive field and extract features of different scales through weighted screening for fusion. The feature fusion module uses adaptive skip connections to enhance the network's ability to extract haze density images to make haze removal more thorough. Furthermore, the decoding module performs non-linear mapping on the fused features to obtain the haze density image, and then restores the haze-free image. RESULTS The experimental results show that the proposed method has achieved better objective and subjective evaluation results, and has a better dehazing effect. CONCLUSION We proposed ERAN method not only provides visually better images, but also helps experts better diagnose other retinal diseases in cataract patients, leading to better care and treatment.
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Affiliation(s)
- Defu Qiu
- Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yuhu Cheng
- Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xuesong Wang
- Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
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Ge H, Zhu Z, Dai Y, Liu R. Super-resolution reconstruction of biometric features recognition based on manifold learning and deep residual network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106822. [PMID: 35667333 DOI: 10.1016/j.cmpb.2022.106822] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 04/10/2022] [Accepted: 04/17/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE In daily life, face information has the characteristics of uniqueness and universality. However, in a real-world scene, the image information of the face acquired by the acquisition device often contains noises such as blurring and sharpening. As such, super-resolution reconstruction of face features recognition based on manifold learning is proposed in this paper. METHODS We reconstruct low-resolution facial expression images, introduce a simplified residual block network and manifold learning, and propose joint supervision through a new hybrid loss function, which not only retains the color and characteristics of the image, but also retains the high-frequency information. The ResNet50 network uses the weight feature of information entropy to optimize the information of the pooling layer, and the esNet50 network uses the improved PSO algorithm to optimize the initial weight of the error back-propagation phase. RESULTS In the case of inputting extremely low resolution (6 × 6) facial expression images, the accuracy rate is increased by 9.091%. The accuracy of the high-resolution facial expressions after reconstruction with a size of 12×12 is 96.970%. The accuracy rate for happy expressions is 100%, the accuracy rate for anger, disgust, sadness, and surprise recognition is 97%, the accuracy rate for contempt is 94%, and the accuracy rate for fear is 88%. CONCLUSIONS The experimental results verify the feasibility and superiority of the system, and effectively improve the accuracy of low-resolution facial expressions.
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Affiliation(s)
- Huilin Ge
- School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
| | - Zhiyu Zhu
- School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
| | - Yuewei Dai
- School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
| | - Runbang Liu
- School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
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Jia H, Chen X, Han Z, Liu B, Wen T, Tang Y. Nonconvex Nonlocal Tucker Decomposition for 3D Medical Image Super-Resolution. Front Neuroinform 2022; 16:880301. [PMID: 35547860 PMCID: PMC9083114 DOI: 10.3389/fninf.2022.880301] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Limited by hardware conditions, imaging devices, transmission efficiency, and other factors, high-resolution (HR) images cannot be obtained directly in clinical settings. It is expected to obtain HR images from low-resolution (LR) images for more detailed information. In this article, we propose a novel super-resolution model for single 3D medical images. In our model, nonlocal low-rank tensor Tucker decomposition is applied to exploit the nonlocal self-similarity prior knowledge of data. Different from the existing methods that use a convex optimization for tensor Tucker decomposition, we use a tensor folded-concave penalty to approximate a nonlocal low-rank tensor. Weighted 3D total variation (TV) is used to maintain the local smoothness across different dimensions. Extensive experiments show that our method outperforms some state-of-the-art (SOTA) methods on different kinds of medical images, including MRI data of the brain and prostate and CT data of the abdominal and dental.
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Affiliation(s)
- Huidi Jia
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xi'ai Chen
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
| | - Zhi Han
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
| | - Baichen Liu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Tianhui Wen
- School of Professional Studies, Columbia University, New York, NY, United States
| | - Yandong Tang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
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Propofol Anesthesia Depth Monitoring Based on Self-Attention and Residual Structure Convolutional Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8501948. [PMID: 35132332 PMCID: PMC8817884 DOI: 10.1155/2022/8501948] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/24/2021] [Accepted: 01/04/2022] [Indexed: 11/17/2022]
Abstract
Methods We compare nine index values, select CNN+EEG, which has good correlation with BIS index, as an anesthesia state observation index to identify the parameters of the model, and establish a model based on self-attention and dual resistructure convolutional neural network. The data of 93 groups of patients were selected and randomly grouped into three parts: training set, validation set, and test set, and compared the best and worst results predicted by BIS. Result The best result is that the model's accuracy of predicting BLS on the test set has an overall upward trend, eventually reaching more than 90%. The overall error shows a gradual decrease and eventually approaches zero. The worst result is that the model's accuracy of predicting BIS on the test set has an overall upward trend. The accuracy rate is relatively stable without major fluctuations, but the final accuracy rate is above 70%. Conclusion The prediction of BIS indicators by the deep learning method CNN algorithm shows good results in statistics.
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Han M, Liu L, Hu M, Liu G, Li P. Medical expert and machine learning analysis of lumbar disc herniation based on magnetic resonance imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 213:106498. [PMID: 34758430 DOI: 10.1016/j.cmpb.2021.106498] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 10/19/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE Observation and statistical analysis was used to evaluate the ability of lumbar disc magnetic resonance imaging (MRI) to obtain the smallest size of Al2O3 spots (calcified foci) and lumbar disc fiber signals. METHODS First, we perform image acquisition of the MRI, perform the statistical analysis using five different sizes of Al2O3 spots and lumbar disc fibers on the imaging plate (IP), use a molybdenum target MRI machine 26 kV, adjust the milliampere amount, select the appropriate image processing parameters, and obtain the experimental image of the density value (D=0.70±0.05), the 5-point judgment method is used to obtain the total score of 10 lines of signals composed of 5 signals and noise, and a group is computed using the statistical analysis that is built from human observation and machine prediction (based on machine learning), which are then compared. In particular, we implemented a convolutional neural network algorithm to evaluate the medical condition against human observers, so as to study the structure of the lumbar intervertebral disc. We compute the true positive probability P(S/s) and false positive probability P(S/n) values, draw ROC curve, and compute the judgment probability value of each signal Pdet. We then use SPSS 10.0 statistical single factor analysis of variance software to process the data, and obtain the smallest calcified focus and lumbar disc mass focus. RESULTS Using probability statistical methods to obtain the data of the ROC curve and the average value of the judgment probability Pdet, among 5 different sizes Al2O3 spots (calcifications), 0.20mm Pdet= 0.6250minimum, 0.55mm Pdet = 0.9000 the largest, but the difference between 0.20mm and 0.25mm Pdet is not statistically significant, and the difference is statistically significant; among the five types of lumbar disc fibers (tumor foci) of different sizes, 0.45mm Pdet= 0.5313minimum, 1.00mm Pdet =0.8813 is the largest, while the difference between 0.45mm and 0.60mm is not statistically significant, and the difference between 0.45mm and other is statistically significant. We note that the human observation and machine learning prediction is not significantly different (P<0.05). CONCLUSIONS The computation of the ROC curve and that of the probability of judgment using the statistical analysis based on a deep learning platform is simple and fast, and approximates that of human observation. It is suitable for the evaluation of image quality control carried out by daily clinical work.
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Affiliation(s)
- Meng Han
- Spinal Surgery Department, Xuzhou Central Hospital, Xuzhou, Jiangsu 221009, China
| | - Lei Liu
- Spinal Surgery Department, Xuzhou Central Hospital, Xuzhou, Jiangsu 221009, China
| | - Mengzi Hu
- Spinal Surgery Department, Xuzhou Central Hospital, Xuzhou, Jiangsu 221009, China
| | - Guangpu Liu
- Spinal Surgery Department, Xuzhou Central Hospital, Xuzhou, Jiangsu 221009, China.
| | - Peipei Li
- Endocrinology Department, The Affiliated Hospital of Xuzhou Medical University, Jiangsu 221002, China.
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